Multiple-wavelength physiological monitor

ABSTRACT

A physiological monitor for determining blood oxygen saturation of a medical patient includes a sensor, a signal processor and a display. The sensor includes at least three light emitting diodes. Each light emitting diode is adapted to emit light of a different wavelength. The sensor also includes a detector, where the detector is adapted to receive light from the three light emitting diodes after being attenuated by tissue. The detector generates an output signal based at least in part upon the received light. The signal processor determines blood oxygen saturation based at least upon the output signal, and the display provides an indication of the blood oxygen saturation.

RELATED APPLICATIONS

This application claims priority from U.S. Provisional No. 61/318,735,filed Mar. 29, 2010 and is a continuation-in-part of U.S. applicationSer. No. 12/045,309, filed Mar. 10, 2008, which is a continuation ofU.S. application Ser. No. 11/139,291, filed May 27, 2005, now U.S. Pat.No. 7,343,186, which claims priority from U.S. Provisional No.60/586,069, filed Jul. 7, 2004. All of the foregoing are expresslyincorporated by reference herein.

BACKGROUND

1. Field

The present invention relates to the field of signal processing. Morespecifically, the present invention relates to the processing ofmeasured signals which contain a primary signal portion and a secondarysignal portion for the removal or derivation of either signal portion.The present invention is especially useful for physiological monitoringsystems, including blood oxygen saturation measurement systems andoximeters.

2. Description of the Related Art

Blood oxygen saturation measurement systems, oximeters, andphysiological monitors of the prior art generally utilize two differentwavelengths of light to determine a patient's blood oxygen saturationlevel. In general, such systems provide two wavelengths of light to atarget location on a patient's body. The systems then measure at leastone signal indicative of the transmission or reflection of the two lightwavelengths with respect to the tissue at the target location.

One such physiological monitor is taught by Diab et al. in U.S. Pat. No.5,632,272, incorporated by reference herein in its entirety. Oneembodiment of Diab's physiological monitor provides light having a redwavelength and light having an infrared wavelength to one side of apatient's finger. A detector on the opposite side of the patient'sfinger measures the red and infrared wavelength light transmittedthrough the patient's finger and generates a measurement signal. Aprocessor analyzes the measurement signal to determine red and infraredcomponent signals. Possible saturation values are input to a saturationequation module which provides reference coefficients. The red orinfrared component signal is processed with the reference coefficientsto yield reference signal vectors.

The reference signal vectors and the red or infrared component signalare processed by a correlation canceller to generate output vectors. Theoutput vectors are input into a master power curve module, whichprovides a blood oxygen saturation value for each possible saturationvalue input to the saturation equation module. The patient's bloodoxygen saturation is determined based upon the power curve moduleoutput.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an arterial blood oxygen saturation curve inaccordance with the prior art.

FIG. 2 illustrates an arterial blood oxygen saturation curve of amulti-wavelength physiological monitor in accordance with one embodimentof the present invention.

FIG. 3 is an example of a physiological monitor in accordance with oneembodiment of the present invention.

FIG. 3A illustrates one embodiment of the low noise emitter currentdriver of the physiological monitor of FIG. 3.

FIG. 4 illustrates one embodiment of the front end analog signalconditioning circuitry and the analog to digital conversion circuitry ofthe physiological monitor of FIG. 3.

FIG. 5 illustrates one embodiment of the digital signal processingcircuitry of FIG. 3.

FIG. 6 illustrates one embodiment of additional operations performed bythe digital signal processing circuitry of FIG. 3.

FIG. 7 illustrates one embodiment of the demodulation module of FIG. 6.

FIG. 8 illustrates one embodiment of the sub-sampling module of FIG. 6.

FIG. 9 illustrates one embodiment of the statistics module of FIG. 6.

FIG. 10 illustrates a block diagram of the operations of one embodimentof the saturation transform module of FIG. 6.

FIG. 10A illustrates an adaptive noise filter that may be used as themulti-variate process estimator of FIG. 10.

FIG. 11 illustrates a saturation transform curve in accordance with oneembodiment of the present invention.

FIG. 12 illustrates a block diagram of the operation of one embodimentof the saturation calculation module of FIG. 6.

DETAILED DESCRIPTION

Spectroscopy is a common technique for measuring the concentration oforganic and some inorganic constituents of a solution. The theoreticalbasis of this technique is the Beer-Lambert law, which states that theconcentration c_(i) of an absorbent in solution can be determined by theintensity of light transmitted through the solution, knowing thepathlength d_(λ), the intensity of the incident light I_(0,λ), and theextinction coefficient ε_(i,λ) at a particular wavelength λ. Ingeneralized form, the Beer-Lambert law is expressed as:

$\begin{matrix}{I_{\lambda} = {I_{0,\lambda}^{{- d_{\lambda}} \cdot \mu_{0,\lambda}}}} & (1) \\{\mu_{0,\lambda} = {\sum\limits_{i = 1}^{n}\; {ɛ_{i,\lambda} \cdot c_{i}}}} & (2)\end{matrix}$

where μ_(0,λ) is the bulk absorption coefficient and represents theprobability of absorption per unit length. The minimum number ofdiscrete wavelengths that may be required to solve Equations 1 and 2 isat least the number of significant absorbers that are present in thesolution. Least squares or other estimation techniques can be used toapproximate a solution to these equations for underdetermined oroverdetermined systems. The system of equations is underdetermined ifthere are fewer equations or wavelengths than unknowns or significantabsorbers (e.g., blood constituents). Conversely, the system isoverdetermined if there are more equations than unknowns.

A practical application of this technique is pulse oximetry, whichutilizes a noninvasive sensor to measure blood oxygen saturation (SpO₂)and pulse rate. A multi-wavelength physiological monitor in accordancewith one embodiment determines blood oxygen saturation by propagatingmulti-wavelength energy through a medium, such as a portion of apatient's body where blood flows close to the body surface. For example,in one embodiment, energy is propagated through an ear lobe, a digit(such as a finger or toe), a forehead, or a scalp (such as a fetus'sscalp). An attenuated signal is measured after energy propagationthrough, or reflection from the medium. The physiological monitordetermines the saturation of oxygenated blood in the patient based atleast in part upon the measured signal.

It is well known by those of skill in the art that freshly oxygenatedblood is pumped at high pressure from the heart into the arteries foruse by the body. The volume of blood in the arteries varies with theheartbeat. This variation gives rise to a variation in energy absorptionat the heartbeat rate, or the pulse.

Oxygen depleted, or deoxygenated, blood is returned to the heart throughthe veins with unused oxygenated blood. Unlike the arteries, the volumeof blood in the veins varies with the rate of breathing, which istypically much slower than the heartbeat. Since the blood pressure inthe veins is typically much lower than that of the arteries, the volumeof blood in the veins varies in response to motion, such as a patientraising or lowering her arm. Changes in blood volume within the veinscause changes in vein thicknesses. Therefore, when there is no motioninduced variation in the thickness of the veins, venous blood causes alow frequency variation in energy absorption, which is related to therate of breathing. However, when erratic, motion-induced variations inthe thickness of the veins occur, the low frequency variation inabsorption is coupled with an erratic variation in energy absorption dueto the erratic motion.

In one embodiment, absorption measurements are based upon thetransmission of energy through a medium. In one embodiment, multiplelight emitting diodes (LEDs) are positioned on one side of a portion ofthe body where blood flows close to the body's surface, such as afinger, and a photodetector is positioned on the opposite side of thesurface. In another embodiment one or more such LEDs emit light ofdifferent wavelengths. In one embodiment, one LED emits a visiblewavelength, such as red, and the other LED emits an infrared wavelength.However, one skilled in the art will realize that other wavelengthcombinations could be used.

The finger comprises skin, tissue, muscle, both arterial blood andvenous blood, fat, etc., each of which absorbs light energy differentlydue to different absorption coefficients, different concentrations,different thicknesses, and changing optical pathlengths. When thepatient is not moving, absorption is substantially constant except forvariations due to the flow of blood through the skin, tissue, muscle,etc. A constant attenuation can be determined and subtracted from themeasured signal via traditional filtering techniques. However, when thepatient moves, perturbations such as changing optical pathlengths occur.Such perturbations may be due to movement of background fluids, (such asvenous blood, which has a different saturation than arterial blood).Therefore, the measured signal becomes erratic. Erratic, motion-inducednoise typically cannot be predetermined and subtracted from the measuredsignal via traditional filtering techniques. Thus, determining theoxygen saturation of arterial blood and venous in erratic,motion-induced noise environments, blood becomes more difficult.

In one embodiment, a physiological monitor measures light transmissionthrough a patient's finger to determine arterial blood oxygensaturation. In some cases, however, the measured light signal containsnoise, or other secondary signal, due to an event, such as patientmovement during signal measurement. In such case, the signal measured bythe physiological monitor includes a primary portion, related to theblood oxygen saturation of the patient, and a secondary portion, relatedto the noisy, erratic, motion-induced secondary signal. Thephysiological monitor processes the measured signal to determine thepatient's blood oxygen saturation based upon the signal's primaryportion.

In one embodiment, the physiological monitor utilizes a processor todetermine a secondary reference signal n′(t) or N_(ref). The secondaryreference signal n′(t) is used to determine the primary portion of themeasured signal. In one embodiment, the secondary reference signal n′(t)is input to a multi-variate process estimator, which removes theerratic, motion-induced secondary signal portions from the measuredsignal. In another embodiment, the processor determines a primary signalreference signal s′(t) which is used for display purposes or for inputto a multi-variate process estimator to derive information about patientmovement and venous blood oxygen saturation.

FIG. 1 illustrates an arterial blood oxygen saturation curve 100representative of the sensitivity of blood oxygen saturation systems ofthe prior art. Such systems utilize two different wavelengths of light(such as red and infrared wavelength light) to determine blood oxygensaturation. The wavelengths used may be, for example, about 660 nm andabout 905 nm. At 660 nm, deoxyhemoglobin has a higher absorption thanoxyhemoglobin, while the reverse is the case at 905 nm. This differencein absorption between oxyhemoglobin and deoxyhemoglobin allows for thecalculation of blood oxygen saturation levels.

In FIG. 1, arterial blood oxygen saturation (SaO₂) is represented on they-axis of the curve 100. The x-axis represents ratios of a redwavelength light transmission signal and an infrared wavelength lighttransmission signal. An ideal saturation curve 102 would be highlyaccurate at all values. However, due to the limitations of using onlytwo wavelengths of light, such systems typically operate between a lowerrange curve 104 and an upper range curve 106. Such blood oxygensaturation systems typically exhibit highly accurate, low tolerance 107measurements at high saturation values 108, but at lower saturationvalues 110, that accuracy decreases, and increased tolerance 111results.

The arterial blood oxygen saturation curve 120 of a multi-wavelengthphysiological monitor in accordance with one embodiment of the presentinvention is shown in FIG. 2. The multi-wavelength system utilizes atleast three different wavelengths of light (λ₁, λ₂, . . . λ_(n)) todetermine blood oxygen saturation. Arterial blood oxygen saturation(S_(a)O₂) is represented on the y-axis of the curve 120. The x-axisrepresents ratios of composite signals, each comprising signals basedupon the light transmission of the various light wavelengths (λ₁, λ₂, .. . λ_(n)). Each ratio r can be expressed by:

$r = \frac{\sum\limits_{i = 1}^{n}\; {\alpha_{i}{NP}_{{RMS},i}}}{\sum\limits_{i = 1}^{n}\; {\beta_{i}{NP}_{{RMS},i}}}$

where n is the number of wavelengths of light utilized by themulti-wavelength physiological monitor, NP_(RMS,i) is the normalizedplethysmographic waveform of the ith wavelength light source, and α_(i)and β_(i) are vector coefficients of known constants that are determinedbased upon fitting and/or calibration using experimental data and/ormodel(s).

The curve 120 contains lower and upper limit range curves 122, 124.However, the lower and upper limit range curves 122, 124 of themulti-wavelength physiological monitor are more linear than the lowerand upper range curves 104, 106 of the dual-wavelength blood oximeterdescribed above, as illustrated in FIG. 1. In addition, the lower andupper limit range curves 122, 124 of the multi-wavelength physiologicalmonitor exhibit higher accuracy and lower tolerance 126 than thedual-wavelength blood oximeter of FIG. 1, particularly at lower arterialblood oxygen saturation levels. In certain cases, such as during themonitoring of neonates, or persons with low blood-oxygen saturation, itis desirable for the physiological monitor to exhibit increased accuracyat lower saturation levels. For example, when providing oxygen to aneonate, it is often critical that neither too much oxygen nor toolittle oxygen is provided. A multi-wavelength physiological monitorprovides improved accuracy as well as an improved signal-to-noise ratioat lower perfusion levels.

A multi-wavelength physiological monitor provides additional advantagesover a two-wavelength device, as well. For example, utilizing multiplewavelengths provide a multi-dimensional calibration curve, which can beused to provide multiple degrees of freedom to compensate for variationin other physiologically-related parameters. As discussed above, whenonly two wavelengths are used but there are more than two significantabsorbers in the patient's tissue, the system may be underdetermined.Therefore, adding wavelengths sensitive to additional significantabsorbers can help the system compensate for the additional significantabsorbers and enable more accurate calculation of the concentration ofeach absorber and the blood oxygen saturation. For example, an eightwavelength physiological monitor utilizes an eight-dimensionalcalibration curve, which provides eight degrees of freedom to compensatefor various physiologically-related parameters. Such parameters caninclude, for example, noise, motion, various hemodynamic parameters,and/or blood constituent concentrations and/or levels. On the otherhand, the system may be overdetermined if, for example, more wavelengthsare used than there are significant absorbers and/or wavelengths areused for constituents not present in the particular patient's blood.

Furthermore, traditional physiological monitors that utilize two lightsources to derive a patient's plethysmographic signal generally requireone light source's wavelength to fall in the red spectrum and the otherlight source's wavelength to fall in the infrared spectrum. However, amulti-wavelength physiological monitor advantageously provides theability to utilize all infrared wavelength light sources and/or othernon-red infrared light sources to derive an accurate plethysmographicwaveform. One advantage of utilizing non-red wavelength light sources(for example, all infrared wavelength light sources) is that themulti-wavelength physiological monitor can be further configured todetermine several blood constituent concentrations based upon signalsmeasured from just the non-red light sources. For example, such amulti-wavelength physiological monitor can determine levels and/orconcentrations of: methemoglobin (MetHb), carboxyhemoglobin (COHb), lowhemoglobin levels, high hemoglobin levels, bilirubin, methylene blue,deoxyhemoglobin, and lipids.

The ability to measure a physiological parameter with amulti-dimensional system allows the system to compensate and account forthe presence of other conditions that may affect the particularphysiological parameter being measured. For example, many drugs causethe level of MetHb within a patient's blood to increase. MetHb alsoabsorbs light at the two wavelengths used in a typical two-wavelengthphysiological monitor, which may cause inaccurate S_(p)O₂ readings.Increased levels of MetHb generally cause blood oxygen concentrationlevel (e.g., S_(p)O₂) readings to decrease. A two-wavelengthphysiological monitor would provide reduced readings of blood oxygenconcentration, but would not be able to identify the cause for thedecreased reading, or to compensate for such readings. On the otherhand, a multi-wavelength physiological monitor, such as any of themulti-wavelength physiological monitors described below, could not onlyidentify the cause of decreased S_(p)O₂ readings, but also (oralternatively) compensate for such cause when calculating the patient'sS_(p)O₂ level and/or signal. For example, to compensate for MetHb, oneor more wavelengths sensitive to MetHb can be used. The wavelength(s)used can be, for example, about 760 nm and/or about 805 nm or the like.This allows for one or more additional equation (s) and degree(s) offreedom so that solving the system may account for the effect of thelevel of MetHb on blood oxygen saturation readings.

Similarly, the system can compensate for other blood constituents withthe addition of wavelengths sensitive to the other blood constituents ofinterest. For example, carboxyhemoglobin absorbs about the same amountof 660 nm light as oxyhemoglobin. Therefore, a typical two-wavelengthphysiological monitor can mistake carboxyhemoglobin for oxyhemoglobin,which can result in normal blood oxygen saturation level readings eventhough the actual level is abnormal. For example, for every about 1% ofcarboxyhemoglobin circulating in the blood, the monitor may over read byabout 1%. Selecting one or more additional wavelengths sensitive tocarboxyhemoglobin, for example, about 610, 630, and/or 640 or the like,can allow the system to compensate for this effect. The system cancompensate for other blood constituents, such as deoxyhemoglobin orlipids, with the addition of wavelengths sensitive to thoseconstituents. More wavelengths can allow the system to compensate formore variables.

The various blood constituents mentioned above may not be orthogonal;rather they are often highly correlated with one another. Therefore,even using multiple wavelengths may not fully eliminate the effect ofsuch constituents on the blood oxygen saturation level calculations, butit can reduce or minimize the effect and produce more accurate bloodoxygen saturation level readings. In one embodiment, using eightwavelengths (including, for example, any of the wavelengths mentionedherein) can provide relatively more accurate readings of blood oxygensaturation levels relative to other numbers of wavelengths. However, thenumber of wavelengths that is most appropriate for a particular patientand that will provide the most accurate readings can vary based on suchfactors as the actual concentrations of blood constituents in thepatient's blood, as well as the patient's condition, gender, age, or thelike. In addition to improving the accuracy of blood oxygen saturationlevel readings, using multiple wavelengths can also provide relativelymore accurate measurements of other parameters, including, for example,the concentrations of any of the blood constituents or other parametersmentioned herein.

In one embodiment, the multi-wavelength physiological monitorcompensates for conditions, such as motion and/or blood constituentsthat would cause a standard two-wavelength system to over- orunder-react to the presence of such condition. Such compensationprovides a more accurate, stable reading or a patient's physiologicalcondition, including, but not limited to, their blood oxygenconcentration and/or plethysmographic signal.

A schematic of one embodiment of a multi-wavelength physiologicalmonitor for pulse oximetry is shown in FIGS. 3-5. FIG. 3 depicts ageneral hardware block diagram of a multi-wavelength pulse oximeter 299.A sensor 300 has n LEDs 302, which in one embodiment are at least threelight emitters. The n LEDs 302 emit light of different wavelengths (λ₁,λ₂, . . . λ_(n)). In one embodiment, the n LEDs 302 include four, six,eight or sixteen LEDs of different wavelengths. In one embodiment, the nLEDs 302 are placed adjacent a finger 310. A photodetector 320 receiveslight from the n LEDs 302 after it has been attenuated by passingthrough the finger. The photodetector 320 produces at least oneelectrical signal corresponding to the received, attenuated light. Inone embodiment, the photodetector 320 is located opposite the n LEDs 302on the opposite side of the finger 310. The photodetector 320 isconnected to front end analog signal conditioning circuitry 330.

The front end analog signal conditioning circuitry 330 has outputs thatare coupled to an analog to digital conversion circuit 332. The analogto digital conversion circuit 332 has outputs that are coupled to adigital signal processing system 334. The digital signal processingsystem 334 provides desired parameters as outputs for a display 336.Outputs for the display 336 include, for example, blood oxygensaturation, heart rate, and a clean plethysmographic waveform.

The signal processing system also provides an emitter current controloutput 337 to a digital-to-analog converter circuit 338. Thedigital-to-analog converter circuit 338 provides control information toemitter current drivers 340. The emitter drivers 340 are coupled to then light emitters 302. The digital signal processing system 334 alsoprovides a gain control output 342 for front end analog signalconditioning circuitry 330.

FIG. 3A illustrates one embodiment of the drivers 340 and the digital toanalog conversion circuit 338. As depicted in FIG. 3A, thedigital-to-analog conversion circuit 338 includes first and second inputlatches 321, 322, a synchronizing latch 323, a voltage reference 324,and a digital to analog conversion circuit 325. The emitter currentdrivers 340 include first and second switch banks 326, 327, and nvoltage to current converters 328. LED emitters 302 of FIG. 3 arecoupled to the output of the emitter current drives 340.

The driver depicted in FIG. 3A is advantageous in that the presentinventors recognized that much of the noise in the oximeter 299 of FIG.3 is caused by the LED emitters 302. Therefore, the emitter drivercircuit of FIG. 3A is designed to minimize the noise from the emitters302. The first and second input latches 321, 322 are connected directlyto the DSP bus. Therefore, these latches significantly minimizes thebandwidth (resulting in noise) present on the DSP bus which passesthrough to the driver circuitry of FIG. 3A. The output of the first andsecond input latches only changes when these latched detect theiraddress on the DSP bus. The first input latch 321 receives the settingfor the digital to analog converter circuit 325. The second input latch322 receives switching control data for the switch banks 326, 327. Thesynchronizing latch 323 accepts the synchronizing pulses which maintainsynchronization between the activation of emitters 302 and the analog todigital conversion circuit 332.

The voltage reference is chosen as a low noise DC voltage reference forthe digital to analog conversion circuit 325. In addition, in thepresent embodiment, the voltage reference has a lowpass output filterwith a very low corner frequency (e.g., 1 Hz in the present embodiment).The digital to analog converter 325 also has a lowpass filter at itsoutput with a very low corner frequency (e.g., 1 Hz). The digital toanalog converter provides signals used to drive each of the emitters302.

In the present embodiment, the output of the voltage to currentconverters 328 are switched such that, only one emitter is active at anygiven time. In addition, the voltage to current converter for theinactive emitter is switched off at its input as well, such that it isdeactivated. This reduces noise from the switching and voltage tocurrent conversion circuitry. In the present embodiment, low noisevoltage to current converters 328 are selected (e.g., Op 27 Op Amps),and the feedback loop is configured to have a low pass filter to reducenoise. In the present embodiment, the low pass filtering function of thevoltage to current converters 328 has a corner frequency of just above625 Hz, which is the switching speed for the emitters 302, as furtherdiscussed below. Accordingly, the driver circuit embodiment of FIG. 3A,minimizes the noise of the emitters 302.

In general, the n light emitters 302 each emit light energy of adifferent wavelength, which is absorbed by the finger 310 and receivedby the photodetector 320. The photodetector 320 produces an electricalsignal which corresponds to the intensity of the light energy strikingthe photodetector 320. The front end analog signal conditioningcircuitry 330 receives the intensity signals and filters and conditionsthese signals as further described below for further processing. Theresultant signals are provided to the analog-to-digital conversioncircuitry 332, which converts the analog signals to digital signals forfurther processing by the digital signal processing system 334. Thedigital signal processing system 334 utilizes the signals in order toprovide what will be called herein a “saturation transform.” It shouldbe understood, that for parameters other than blood saturationmonitoring, the saturation transform could be referred to as aconcentration transform, in-vivo transform, or the like, depending onthe desired parameter. The term “saturation transform” is used todescribe an operation which converts the sample data from time domain tosaturation domain values, as will be apparent from the discussion below.In the present embodiment, the output of the digital signal processingsystem 334 provides clean plethysmographic waveforms of the detectedsignals and provides values for oxygen saturation and pulse rate to thedisplay 336.

It should be understood that in different embodiments of the presentinvention, one or more of the outputs may be provided. The digitalsignal processing system 334 also provides control for driving the nlight emitters 302 with an emitter current control signal on the emittercurrent control output 337. This value is a digital value which isconverted by the digital-to-analog conversion circuit 338, whichprovides a control signal to the emitter current drivers 340. Theemitter current drivers 340 provide the appropriate current drive forthe n light emitters 302. Further detail of the operation of themulti-wavelength physiological monitor for pulse oximetry is explainedbelow.

In the present embodiment, the n light emitters 302 are driven via theemitter current driver 340 to provide light transmission with digitalmodulation at 625 Hz. In the present embodiment, the n light emitters302 are driven at a power level that provides an acceptable intensityfor detection by the detector 320 and for conditioning by the front endanalog signal conditioning circuitry 330. Once this energy level isdetermined for a given patient by the digital signal processing system334, the current level for the n light emitters 302 is maintainedsubstantially constant. It should be understood, however, that thecurrent could be adjusted for changes in the ambient room light andother changes that would affect the voltage input to the front endanalog signal conditioning circuitry 330.

In one embodiment, the n light emitters 302 are modulated as follows:for one complete 625 Hz cycle, each emitter 302 is activated for one ½ncycle, and off for the remaining (2n−1)/2n cycle. In order to onlyreceive one signal at a time 302, the emitters are cycled on and offalternatively, in sequence, with each only active for a ½n cycle per 625Hz cycle, with a ½n cycle separating the active times. The light signalis attenuated (e.g., amplitude modulated) by the pumping of bloodthrough the finger 310 (or other sample medium). The attenuated (e.g.,amplitude modulated) signal is detected by the photodetector 320 at the625 Hz carrier frequency for the multi-wavelength light. Because only asingle photodetector 320 is used, the photodetector 320 receives alllight wavelength signals to form a composite time division signal.

The composite time division signal is provided to the front analogsignal conditioning circuitry 330. Additional detail regarding the frontend analog signal conditioning circuitry 330 and the analog to digitalconverter circuit 332 is illustrated in FIG. 4. As depicted in FIG. 4,in one embodiment, the front end circuitry 330 has a preamplifier 342, ahigh pass filter 344, an amplifier 346, a programmable gain amplifier348, and a low pass filter 350. In one embodiment, the preamplifier 342is a transimpedance amplifier that converts the composite current signalfrom the photodetector 320 to a corresponding voltage signal, andamplifies the signal. In the present embodiment, the preamplifier has apredetermined gain to boost the signal amplitude for ease of processing.In the present embodiment, the source voltages for the preamplifier 342are −15 VDC and +15 VDC. As will be understood, the attenuated signalcontains a component representing ambient light as well as a componentrepresenting the each of the multi-wavelengths of light over time. Ifthere is light in the vicinity of the sensor 300 other than themulti-wavelengths of light from the n light emitters 302, this ambientlight is detected by the photodetector 320. Accordingly, the gain of thepreamplifier is selected in order to prevent the ambient light in thesignal from saturating the preamplifier under normal and reasonableoperating conditions.

In one embodiment, the preamplifier 342 includes an Analog DevicesAD743JR OpAmp. This transimpedance amplifier is particularlyadvantageous in that it exhibits several desired features for the systemdescribed, such as: low equivalent input voltage noise, low equivalentinput current noise, low input bias current, high gain bandwidthproduct, low total harmonic distortion, high common mode rejection, highopen loop gain, and a high power supply rejection ratio.

The output of the preamplifier 342 is coupled to an input of the highpass filter 344. The output of the preamplifier also provides a firstinput 346 to the analog to digital conversion circuit 332. In thepresent embodiment, the high pass filter is a single-pole filter with acorner frequency of about ½-1 Hz. However, the corner frequency isreadily raised to about 90 Hz in one embodiment. As will be understood,the 625 Hz carrier frequency of the multi-wavelength light signal iswell above a 90 Hz corner frequency. The high-pass filter 344 has anoutput coupled as an input to an amplifier 346. In the presentembodiment, the amplifier 346 comprises a unity gain amplifier. However,the gain of the amplifier 346 is adjustable by the variation of a singleresistor. The gain of the amplifier 346 is increased if the gain of thepreamplifier 342 is decreased to compensate for the effects of ambientlight.

The output of the amplifier 346 provides an input to a programmable gainamplifier 348. The programmable gain amplifier 348 also accepts aprogramming input from the digital signal processing system 334 on again control signal line 343. In one embodiment, the gain of theprogrammable gain amplifier 348 is digitally programmable. The gain isadjusted dynamically at initialization or at sensor placement due tochanges in the medium (e.g., the finger) and due to variations in themedium from patient to patient. Therefore, a dynamically adjustableamplifier is provided by the programmable gain amplifier 348 in order toobtain a signal suitable for processing.

The programmable gain amplifier 348 is also advantageous in analternative embodiment in which the emitter drive current is heldconstant. In the present embodiment, the emitter drive current isadjusted for each patient in order to obtain the proper dynamic range atthe input of the analog to digital conversion circuit 332. However,changing the emitter drive current can alter the emitter wavelength,which in turn affects the end result in oximetry calculations.Accordingly, in another embodiment, it is advantageous to fix theemitter drive current for all patients. In an alternative embodiment ofthe present invention, the programmable gain amplifier can be adjustedby the DSP in order to obtain a signal at the input to the analog todigital conversion circuit which is properly within the dynamic range(+3 V to −3 V in the present embodiment) of the analog to digitalconversion circuit 332. In this manner, the emitter drive current couldbe fixed for all patients, eliminating wavelength shift due to emittercurrent drive changes.

The output of the programmable gain amplifier 348 couples as an input toa low-pass filter 350. Advantageously, the low pass filter 350 is asingle-pole filter with a corner frequency of approximately 10 kHz inthe present embodiment. This low pass filter provides anti-aliasing inthe present embodiment.

The output of the low-pass filter 350 provides a second input 352 to theanalog-to-digital conversion circuit 332. In the present embodiment, theanalog-to-digital conversion circuit 332 comprises a firstanalog-to-digital converter 354 and a second analog-to-digital converter356. Advantageously, the first analog-to-digital converter 354 acceptsinput from the first input 346 to the analog-to-digital conversioncircuit 332, and the second analog to digital converter 356 acceptsinput on the second input 352 to the analog-to-digital conversioncircuitry 332.

In one embodiment, the first analog-to-digital converter 354 is adiagnostic analog-to-digital converter. The diagnostic task (performedby the digital signal processing system) is to read the output of thedetector as amplified by the preamplifier 342 in order to determine ifthe signal is saturating the input to the high-pass filter 344. In thepresent embodiment, if the input to the high pass filter 344 becomessaturated, the front end analog signal conditioning circuits 330provides a “0” output. Alternatively, in another embodiment, a firstanalog-to-digital converter 354 is not used.

The second analog-to-digital converter 356 accepts the conditionedcomposite analog signal from the front end signal conditioning circuitry330 and converts the signal to digital form. In the present embodiment,the second analog to digital converter 356 comprises a single-channel,delta-sigma converter. In the present embodiment, a CrystalSemiconductor CS5317-KS delta-sigma analog to digital converter is used.Such a converter is advantageous in that it is low cost and exhibits lownoise characteristics. More specifically, a delta-sigma converterconsists of two major portions: a noise modulator and a decimationfilter. The selected converter uses a second order analog delta-sigmamodulator to provide noise shaping. Noise shaping refers to changing thenoise spectrum from a flat response to a response where noise at thelower frequencies has been reduced by increasing noise at higherfrequencies. The decimation filter then cuts out the reshaped, higherfrequency noise to provide 16-bit performance at a lower frequency. Thepresent converter samples the data 128 times for every 16 bit data wordthat it produces. In this manner, the converter provides excellent noiserejection, dynamic range and low harmonic distortion, which help incritical measurement situations like low perfusion and electrocautery.

In addition, by using a single-channel converter, there is no need totune two or more channels to each other. The delta-sigma converter isalso advantageous in that it exhibits noise shaping, for improved noisecontrol. An exemplary analog to digital converter is a CrystalSemiconductor CS5317. In the present embodiment, the second analog todigital converter 356 samples the signal at a 20 kHz sample rate. Theoutput of the second analog to digital converter 356 provides datasamples at 20 kHz to the digital signal processing system 334 (FIG. 3).

The digital signal processing system 334 is illustrated in additionaldetail in FIG. 5. In the present embodiment, the digital signalprocessing system includes a microcontroller 360, a digital signalprocessor (DSP) 362, a program memory 364, a sample buffer 366, a datamemory 368, a read only memory 370 and communication registers 372. Inone embodiment, the digital signal processor 362 is an Analog Devices AD21020, although other digital signal processors may be employed, as iswell known by those of skill in the art. In one embodiment, themicrocontroller 360 comprises a Motorola 68HC05, with built in programmemory. In the present embodiment, the sample buffer 366 is a bufferwhich accepts the 20 kHz sample data from the analog to digitalconversion circuit 332 for storage in the data memory 368. In thepresent embodiment, the data memory 368 comprises 32 kWords (words being40 bits in the present embodiment) of static random access memory. Otherchips, data rates and data memory configurations may be employed, as iswell known to those of skill in the art.

In one embodiment, the microcontroller 360 is coupled to the DSP 362 viaa conventional JTAG Tap line. The microcontroller 360 transmits the bootloader for the DSP 362 to the program memory 364 via the Tap line, andthen allows the DSP 362 to boot from the program memory 364. The bootloader in program memory 364 then causes the transfer of the operatinginstructions for the DSP 362 from the read only memory 370 to theprogram memory 364. Advantageously, the program memory 364 is a veryhigh speed memory for the DSP 362. The microcontroller 360 provides theemitter current control and gain control signals via the communicationsregister 372.

FIGS. 6-10 depict functional block diagrams of the operations of themulti-wavelength pulse oximeter 299 that in one embodiment are executedby the digital signal processing system 334. The signal processingfunctions described below are carried out by the DSP 362 in the presentembodiment, with the microcontroller 360 providing system management. Inthe present embodiment, the operation is software/firmware controlled.FIG. 6 depicts a generalized functional block diagram for the operationsperformed on the 20 kHz sample data entering the digital signalprocessing system 334. As illustrated in FIG. 6, a demodulationoperation, as represented in a demodulation module 400, is firstperformed. Sub-sampling, as represented by sub-sampling operation 402,is then performed on the resulting data. Certain statistics arecalculated, as represented in a statistics module 404. A saturationtransform is performed, as represented in a saturation transform module406, on the data resulting from the sub-sampling operation 402. The datasubjected to the statistics operations and the data subjected to thesaturation transform operations are forwarded to saturation operations,as represented by a saturation calculation module 408 and pulse rateoperations, as represented by pulse rate calculation module 410.

In general, the demodulation operation separates each of themulti-wavelength signals from the composite signal and removes thecarrier frequency, leaving raw data points. The raw data points areprovided at intervals (e.g., at 625 Hz) to the sub-sampling operation402, which in one embodiment, reduces the samples by an order of 10 fromsamples at 625 Hz to samples at 62.5 Hz. The sub-sampling operation alsoprovides some filtering on the samples. The resulting data is subjectedto statistics and saturation transform operations 404, 406 to calculatea saturation value, which is very tolerant to motion artifacts and othernoise in the signal. The saturation value is ascertained in thesaturation calculation module 408, and a pulse rate and a cleanplethysmographic waveform are obtained through the pulse rate module410. Additional details regarding the various operations are provided inconnection with FIGS. 7-10.

FIG. 7 illustrates one embodiment of the operation of the demodulationmodule 400. One embodiment of a composite, modulated signal format isdepicted in FIG. 7. One full 625 Hz cycle of the n-wavelength, compositesignal is depicted in FIG. 7 with the first ½n cycle including the firstlight wavelength signal plus ambient light signal, the second ½n cycleincluding an ambient light signal, the third ½n cycle including thesecond light wavelength signal plus ambient light signal, and the fourth½n cycle including an ambient light signal. In one embodiment, thispattern repeats n times, wherein the (2n−1)^(th) ½ n cycle includes then^(th) light wavelength signal plus an ambient light signal, and the2n^(th) cycle includes an ambient light signal.

Alternatively, in another embodiment, the first cycle of the compositesignal includes the first light wavelength signal plus an ambient lightsignal, and the second cycle of the composite signal includes the secondlight wavelength signal plus an ambient light signal. This patternrepeats to the n^(th) cycle of the composite signal, which includes then^(th) wavelength signal plus an ambient light signal. In suchembodiment, the (n+1)^(th) cycle includes only an ambient light signal.

As depicted in FIG. 7, when a 20 kHz sampling frequency is utilized, thesingle full cycle at 625 Hz described above comprises 32 samples of 20kHz data, eight samples relating to the first wavelength of light plusambient light, eight samples relating to ambient light, eight samplesrelating to the second wavelength of light plus ambient light, finallyeight samples related to ambient light, etc. This pattern repeats foreach of the n wavelength of light.

Because the signal processing system 334 controls the activation of thelight emitters 302, the entire system is synchronous. In one embodiment,the data is synchronously divided (and thereby demodulated) into 2n8-sample packets, with a time division demultiplexing operation asrepresented in a demultiplexing module 421. One eight-sample packet 422represents the first wavelength of light plus ambient light signal, asecond eight-sample packet 424 represents an ambient light signal, athird eight-sample packet (not shown) represents the attenuated secondwavelength of light plus ambient light signal, a fourth eight-samplepacket (not shown) represents the ambient light signal. This structurerepeats until the (2n−1)^(th) eight-sample packet 426, which representsthe attenuated n^(th) wavelength of light plus ambient light signal, and2n^(th) eight-sample packet 428, which represents an ambient lightsignal. A select signal synchronously controls the demultiplexingoperation so as to divide the time-division multiplexed composite signalat the input of the demultiplexer 421 into its n subparts.

In one embodiment, the last several samples from each packet are thenprocessed as follows. A sum of the last four samples from each packet iscalculated, as represented in the summing operations 430 of FIG. 7. Inthe present embodiment, the last four samples are used because a lowpass filter in the analog to digital converter 356 of the presentembodiment has a settling time. However, any number of samples may be sosummed. The selection of the number of samples to be summed will bedetermined based at least in part on the settling time of the n LEDs 302(not shown). Collecting the last four samples from each 8-sample packetallows the previous signal to clear. This summing operation provides anintegration operation which enhances noise immunity. The sum of therespective ambient light samples is then subtracted from the sum of eachof the individual wavelength samples, as represented in the subtractionmodules 438. It should be understood that in one embodiment, for nwavelengths of light, there will be n subtraction modules 438. Thesubtraction operation provides some attenuation of the ambient lightsignal present in the data. In the present embodiment, it has been foundthat approximately 20 dB attenuation of the ambient light is provided bythe operations of the subtraction modules 438. The resultant individualwavelength sum values are divided by the number of samples summed insumming operations 430. In the present embodiment, the sum values aredivided by four, as represented in the divide modules 442. Eachresultant value provides one sample each of each of the individualwavelengths light signals at 625 Hz.

It should be understood that the carrier frequency has been removed bythe demodulation operation 400. In one embodiment, the 625 Hz sampledata at the output of the demodulation operation 400 is sample datawithout the carrier frequency. In order to satisfy Nyquist samplingrequirements, less than 20 Hz is used (understanding that the humanpulse is about 25 to 250 beats per minute, or about 0.4 Hz-4 Hz).Accordingly, the 625 Hz resolution is reduced to 62.5 Hz in thesub-sampling operation 402 (not shown). Although in the presentembodiment the sub-sampling operation 400 effectively reduces the datarate by 10:1, other such sub-sampling ratios may be used. The term“sub-sampling,” in addition to its ordinary meaning, is intended toinclude decimation and sub-sampling at any appropriate rate or ratio.Such methods are well known to those of skill in the art.

FIG. 8 illustrates the operations of one embodiment of the sub-samplingmodule 402 of FIG. 6. The multi-wavelength light sample data is providedat 625 Hz to corresponding buffers or filters 450. It should beunderstood that in one embodiment, for n wavelengths of light, there aren corresponding buffers or filters 450. In the present embodiment, themulti-wavelength light buffers/filters 450 are 519 samples deep,although other such buffers or filters may be used, as is well known tothose of skill in the art. In one embodiment, the buffer filters 450function as continuous first-in, first-out buffers (FIFO buffers). The519 samples of each buffer filter 450 are subjected to low-passfiltering. Preferably, the low-pass filtering has a cutoff frequency ofapproximately 7.5 Hz with attenuation of approximately −110 dB. Thebuffer/filters 450 form a Finite Impulse Response (FIR) filter withcoefficients for 519 taps. In order to reduce the sample frequency byten, the low-pass filter calculation is performed every ten samples, asrepresented in sub-sample modules 454. In other words, with the transferof each new ten samples into the buffer/filters 450 a new low passfilter calculation is performed by multiplying the impulse response(coefficients) by the 519 filter taps. Each filter calculation providesone output sample for respective output buffers 458. In the presentembodiment, the output buffers 458 are also continuous FIFO buffers thathold 570 samples of data. The 570 samples provide respective samples orpackets (also denoted “snapshot” herein) of samples. The output buffers458 provide sample data to the statistics operation module 404,saturation transform module 406, and the pulse rate module 410, asillustrated in FIG. 6.

FIG. 9 illustrates additional functional operation details of thestatistics module 404. In summary, the statistics module 404 providesfirst order oximetry calculations and RMS signal values for each of then wavelengths of light. The statistics module 404 also provides across-correlation output which indicates a cross-correlation between then wavelengths of light.

As represented in FIG. 9, the statistics module 404 receives n packetsof samples from the output buffers 458 (not shown) of the sub-samplingmodule 402 of FIGS. 6 and 8. In one embodiment, the samples include 570samples at 62.5 Hz which represent the attenuated n wavelengths of lightsignals with the carrier frequency removed. Each packet is normalizedwith a log function, as represented in the n log modules 480.

The signals are then subjected to bandpass filtering, as represented inthe n bandpass filter modules 488. In the present embodiment, with 570samples in each packet, the bandpass filters are configured with 301taps to provide a FIR filter with a linear phase response and little orno distortion. In the present embodiment, the bandpass filter has a passband from 34 beats/minute to 250 beats/minute. The 301 taps slide overeach 570 sample packet in order to obtain 270 filtered samples for eachof the n filtered wavelength signal. In one embodiment, the n bandpassfilters 488 remove the DC in the signal. However, in another embodiment,addition DC removal operations (not shown) may be provided to assist inDC removal.

After filtering, the last j samples from each packet (each packet nowcontaining 270 samples in the present embodiment) are selected forfurther processing, as represented in the n select last j samplesmodules 492. In one embodiment, j equals 120, and the last 120 samplesare selected in the select last j samples modules 492. In oneembodiment, 120 samples are selected because the first 150 samples fallwithin the settling time for the saturation transfer module 406. Thesaturation transfer module 406 processes the same data packets, asfurther discussed below.

In the present embodiment, saturation equation calculations areperformed on each 120-sample packet. In the present embodiment, thesaturation calculations are performed in two different ways. For onecalculation, the 120-sample packets are processed to obtain eachpacket's overall RMS value, as represented in the λ₁ through λ_(n) RMSmodules 496. It should be understood that in the present embodimentthere are n such RMS modules, although as few as one RMS module may beused. The resultant RMS values for each of the n wavelengths of lightprovide input values to a first ratio operation 500, which provides itsoutput as an input to a saturation equation module 502. The ratiooperation 500 calculates a ratio of the various signals based upon themulti-wavelength model described above, and illustrated as:

$r = \frac{\sum\limits_{i = 1}^{n}\; {\alpha_{i}{NP}_{{RMS},i}}}{\sum\limits_{i = 1}^{n}\; {\beta_{i}{NP}_{{RMS},i}}}$

The ratio of the intensity of different light wavelengths may be used todetermine the oxygen saturation of the patient. In one embodiment, theratio is provided to a saturation equation module 502, which includes alook-up table, a polynomial, or the like. The saturation equation module502 provides a saturation values at its output 504 based upon the ratio.In another embodiment, the n wavelengths' individual RMS values are alsoprovided as outputs of the statistics operations module 404.

The n 120-sample packets (corresponding to each of the n wavelengths oflight) are subjected to a cross-correlation operation as represented ina first cross-correlation module 506. The first cross-correlation module506 determines if good correlation exists between the various lightwavelength signals. This cross correlation is advantageous for detectingdefective or otherwise malfunctioning detectors. The cross correlationis also advantageous in detecting when the signal model is satisfied.The signal model of the multi-wavelength physiological monitor isdescribed in greater detail above with respect to FIG. 2, and in greaterdetail below, with respect to FIG. 10. If correlation becomes too lowbetween the signals, the signal model is not satisfied. In order todetermine whether the signal model is satisfied, the normalized crosscorrelation can be computed by the cross-correlation module 506 for eachsnapshot of data.

In one embodiment, correlation between any two wavelength signals x₁ andx₂ is determined according to:

$\frac{\sum\limits_{j}\; {x_{1,j}x_{2,j}}}{\sqrt{\sum\limits_{j}\; x_{1,j}}\sqrt{\sum\limits_{j}\; x_{2,j}}}$

For n wavelengths, a cross-correlation matrix, Corr [x x^(T)], isdetermined, where xεR^(n). In one embodiment, a minimum value of thecross-correlation matrix is determined. The minimum value may bedetermined by looking for the minimum value within the matrix, or theminimum eigenvalue of the matrix. Other methods may be used, as are wellknown to those of skill in the art.

If the cross-correlation minimum value is too low, the oximeter 299provides a warning (e.g., audible, visual, etc.) to the operator. In thepresent embodiment, if a selected snapshot yields a normalizedcorrelation of less than 0.75, the snapshot does not qualify. Signalswhich satisfy the signal model will have a correlation greater than thethreshold.

In one embodiment, the 120-sample packets are also subjected to a secondsaturation operation and cross correlation in the same manner asdescribed above, except the 120-sample packets are first divided intoequal bins of samples (e.g., five bins of 24 samples each). The RMS,ratio, saturation, and cross correlation operations are performed on abin-by-bin basis. These operations are represented in the divide intoequal bins modules 510 the second RMS modules 514 the second ratiomodule 518, the second saturation equation module 520, and the secondcross-correlation module 522, as illustrated in FIG. 9. In oneembodiment, the divide into equal bins modules 510 divides the data intofive equal bins. However, the divide into equal bins modules 510 maydivide the data into any desirable number of equal bins, for example, 7bins, 10 bins or 20 bins.

FIG. 10 illustrates additional detail regarding the saturation transformmodule 406 of FIG. 6. As illustrated in FIG. 10, the saturationtransform module 406 includes log module 537, bandpass filter module538, signal selector module 536, reference generator 530, amulti-variate process estimator 531, a master power curve module 554, adivide into equal bins module 556, and a bin power curve module 533. Inanother embodiment, the saturation transform module has a referenceprocessor, a correlation canceller, and an integrator to provide a powercurve for separate signal coefficients. Reference processors,correlation cancellers, and integrators are well known to those of skillin the art, and are described in U.S. Pat. Nos. 5,632,272 and 5,490,505,both of which are incorporated by reference herein in their entireties.

As depicted in FIG. 10, the saturation transform module 406 receives then packets (one packet for each wavelength of light) from thesub-sampling module 402 (not shown) of FIG. 6. In one embodiment, eachof the n packets contains 570 samples, although other sized packets maybe employed. The data stored in the n packets, indicated in FIG. 10 asλ₁ Signal Data, λ₂ Signal Data, . . . λ_(n) Signal Data, is processed bylog module 537. Log module 537 performs a logarithmic function on eachof the n packets, similar to the log module 480 of the statistics module404 described above with reference to FIG. 9. The output of the logmodule 537 is input to a bandpass filter module 538. In one embodiment,the bandpass filter module 538 performs the same type of filtering asthe n bandpass filters 488 of the statistics module 404 described abovewith reference to FIG. 9. Accordingly, each set of 570 samples subjectedto bandpass filtering results in 270 remaining samples. The resultingdata at the n outputs 542 of the bandpass filter module 538 are, in oneembodiment, n vectors of 270 samples. Each output 542 represents thenormalized plethysmographic waveform of the corresponding wavelength oflight. The outputs 542 are provided to a signal selector module 536 anda reference generator 530.

A plurality of possible saturation values (the “saturation axis scan,”or SaO₂ _(—) _(scan) values) are provided to the saturation referenceprocessor 530 in addition to the normalized plethysmographic waveformoutputs 542. In the present embodiment, 117 saturation values areprovided as the saturation axis scan. In a preferred embodiment, the 117saturation values range uniformly from a blood oxygen saturation of 34.8to 105.0. Accordingly, in the present embodiment, the 117 saturationvalues provide an axis scan for the reference generator 530, whichgenerates a reference signal N_(ref) for use by the multi-variateprocess estimator 531.

In the present embodiment, the multi-variate process estimator 531includes a pseudo-inverse, as is known to those of skill in the art. Inanother embodiment, the multi-variate process estimator 531 is formed bya joint process estimator and a low pass filter. Details of a suitablejoint process estimator are provided in U.S. Pat. No. 5,632,272,incorporated by reference herein. However, it will be understood bythose of skill in the art that a variety of such processing structuresmay be utilized. For example, in another embodiment, a correlationcanceller, an adaptive linear combiner, an adaptive noise filter (e.g.,as shown in FIG. 10A), an adaptive noise canceller, an adaptive linearlattice, a neural network, a radial basis, or a voltera are usedindividually or in combination, instead of or in addition to thepseudo-inverse embodiment of the multi-variate process estimator 531.Such processing structures are well known to those of skill in the art,and require no further explanation herein.

It should be understood that the scan values could be chosen to providehigher or lower resolution than 117 scan values. In one embodiment, thescan values are non-uniformly spaced.

As illustrated in FIG. 10, the reference processor 530 accepts thesaturation axis scan values as an input and provides a reference signalN_(ref) as an output. However, in another embodiment, saturation axisscan values are provided to a saturation equation module (not shown). Insuch embodiment, the saturation equation module provides outputs “r_(n)”that correspond to the plurality of scan value discussed above. Thesaturation equation simply provides a known ratio that corresponds tothe saturation value received as an input based upon data containedwithin a look-up table, or based upon a known polynomial relationshipbetween the saturation axis scan values and the output “r_(n)”.

When a saturation equation module is employed, the ratio “r_(n)” isprovided by the saturation equation module as an input to the referencegenerator 530, along with the sample packets for each of the n lightwavelengths. When a saturation equation module is not employed, asillustrated in FIG. 10, a plurality of “r_(n)” values are provided asthe saturation axis. In one embodiment, the “r_(n)” values arerepresented as ρ(SaO₂ _(—) _(scan)), a row vector of known constants.The reference generator 530 processes the inputs as follows.

The reference generator 530 output N_(ref) is a vector which equalsρ(SaO₂ _(—) _(scan))x, where x is a vector of the normalizedplethysmographic waveforms for each of the n wavelengths of lightsignals x_(i) (such as outputs 542) and ρ(SaO₂ _(—) _(scan)) is a rowvector of known constants.

ρ(SaO₂ _(—) _(scan))=f ⁻¹(SaO₂ _(—) _(scan))b ^(T) −a ^(T) ,ρεR ^(1xn).

This noise reference signal can be derived from the following conditionsand relationships. For example, in one embodiment, arterial blood oxygensaturation (SaO₂) is estimated with the following generalizedratiometric model:

$\begin{matrix}{{{{SaO}\; 2} = {f\left( {\frac{a^{T}x_{a,{rms}}}{b^{T}x_{a,{rms}}} + {bias}} \right)}},{{with}\mspace{14mu} a},b,{x_{a,{rms}} \in {R^{n}\mspace{14mu} {and}\mspace{14mu} {bias}} \in {R.}}} & (1)\end{matrix}$

Within the ratiometric model: (i) a, b and bias are known constantsderived from and/or defined based on fitting and/or calibration usingexperimental data and/or one or more models; (ii) x_(a,rms) is a vectorof arterial rms-normalized plethysmographic data. For example, in thecase of a nine-wavelength system, n=9, and each entry of x_(a) isarterial rms-normalized plethysmographic data associated to a specificwavelength; and (iii) f:R→R is a functional mapping defining a“calibration” curve. It may assume any shape as long as it isinvertible.

In one embodiment, the venous component of the plethysmographic signal,n_(v), is linearly added to the arterial plethysmographic signal suchthat:

x=x _(a) +n _(v) ,x,x _(a) ,n _(v) εR ^(n).  (2)

In the foregoing equation (2), x represents the instantaneous bulknormalized plethysmographic signal, composed of arterial, x_(a), andvenous, n_(v), portions. The venous portion is assumed to be and/orcorresponds to the noise component of the bulk normalizedplethysmographic signal.

In one embodiment, all the entries of n_(v) have the same frequencycontent. For example, if n_(v)=[n₁ n₂ n₃]^(T), thencorr(n₁,n₂)=corr(n₁,n₃)=corr(n₂, n₃)=1, which implies that motionaffects evenly all the wavelengths, since it is caused by a single noisesource (e.g., the change in venous blood volume). Furthermore, x_(a) andn_(v) are assumed to be uncorrelated.

In one embodiment, a venous noise reference signal is derived from theparameters and relationships provided above. For example, replacing inequation (1), the rms version, x_(a,rms), with its instantaneouscounterpart, x_(a), it follows that,

((f ⁻¹(SaO2)−bias)b ^(T) −a ^(T))x _(a)=0.

Replacing x_(a) with (2) yields:

((f ⁻¹(SaO2)−bias)b ^(T) −a ^(T))n _(v)=((f ⁻¹(SaO2)−bias)b ^(T) −a^(T))x.  (3)

It follows from equation (3) that all entries of vector n_(v) can belinearly related to a single source (for example, the change in venousblood due to motion, v_(v)) as follows:

n _(v) =αv _(v) , αεR ^(n), and v _(v) εR.  (4)

In one embodiment, the vector of parameters, α, is not a function ofmotion artifacts, but is instead a function of the physiologicalparameters of the site being used for measurement. Therefore, it hasmuch slower variations when compared to the source of motion, v_(v), andas a result, it can be assumed constant during a fewarterial-plethysmographic cycles.

Replacing (4) in (3) yields:

((f ⁻¹(SaO2)−bias)b ^(T) −a ^(T))αv _(v)=((f ⁻¹(SaO2)−bias)b ^(T) −a^(T))x.  (5)

During the time interval of a few plethysmographic cycles, if SaO2 isassumed to be a constant, it follows from (5) that:

β(SaO2)v _(v)=ρ(SaO2)x,  (6)

where, β is a unknown scalar, is only a function of the saturationvalue, such that:

β(SaO2)=((f ⁻¹(SaO2)−bias)b _(T) −a ^(T))α,βεR,

and ρ is a row vector of known constants, is also only a function of thesaturation value, such that:

ρ(SaO2)=((f ⁻¹(SaO2)−bias)b ^(T) −a ^(T)),ρεR ^(lxn).

Equation (6) implies that if the correct value for SaO2 is applied toit, the venous noise signal will be a linear combination of the bulkplethysmographic signals, entries of vector x.

Therefore, in equation (6), β(SaO2)v_(v) is the noise reference signalthat can be used in combination with an adaptive linear noise cancellerto remove the venous noise signal from the bulk plethysmographic signal.

As a result, the venous-noise-reference-signal equation can be writtenas:

N _(ref)=ρ(SaO2)x  (7)

The vector x is provided as the outputs 542 illustrated in FIG. 10. TheSaO₂ _(—) _(scan) values are the “r_(n)” values provided as thesaturation axis, and a and b are known constants defined based onfitting and/or calibration using experimental data and/or models. Thisoperation is completed for each of the saturation scan values (e.g., 117possible values in the present embodiment). Accordingly, the resultantdata can be described as 117 reference signal vectors of 570 data pointseach, hereinafter referred to as the reference signal vectors. This datacan be stored in an array or any such data structure as is well known tothose of skill in the art.

In the present embodiment, as described above, the outputs 542 are alsoprovided to a signal selector module 536. One of the output signals 542is selected by the signal selector module 536 for further processing bythe multi-variate process estimator 531. The selected signal is referredto as X_(sel). It is understood by those of skill in the art that anyone of the output signals 542 may be selected by the signal selectormodule 536 for further processing.

In one embodiment, the multi-variate process estimator 531 includes apseudo-inverse, which is used to determine a weight vector w associatedwith the reference signal N_(ref) and the selected signal. In oneembodiment, the multi-variate process estimator 531 creates multiplesingle-column vectors of time-shifted data from the reference signalN_(ref). For example, in one embodiment, the multi-variate processestimator 531 creates single-column vectors A, where:

$A = \left\lbrack {{{\begin{bmatrix}N_{{ref\_}1} \\N_{{ref\_}2} \\N_{{ref\_}3} \\N_{{ref\_}4} \\\vdots\end{bmatrix}\begin{bmatrix}N_{{ref\_}7} \\N_{{ref\_}8} \\N_{{ref\_}9} \\N_{{ref\_}10} \\\vdots\end{bmatrix}}\begin{bmatrix}N_{{ref\_}13} \\N_{{ref\_}14} \\N_{{ref\_}15} \\N_{{ref\_}16} \\\vdots\end{bmatrix}}\cdots} \right\rbrack$

In such embodiment, a pseudo-inverse is determined as (A^(T)A)⁻¹A^(T).The weight vector w may then be determined by multiplying thepseudo-inverse by the selected signal (X_(sel)) from the signal selectmodule 536. The resulting vector w may be expressed as:w=(A^(T)A)⁻¹A^(T)x_(sel). The output vectors w of the multi-variateprocess estimator 531 are provided to a master power curve module 554and a divide into equal bins module 556.

The divide into equal bins module 556 divides each of the output vectorsinto bins having equal numbers of data points. In one embodiment, thedivide into equal bins module 556 divides each of the output vectorsinto five bins, each containing the same number of data points (e.g.,with 120 data points per vector, each bin could have 24 data points).Each bin is then provided to a bin power curves module 558.

In one embodiment, the master power curve module 554 performs asaturation transform as follows. For each output vector, the sum of thesquares of the data points is determined. This provides a sum of squaresvalue corresponding to each output vector (each output vectorcorresponding to one of the saturation scan values). These valuesprovide the basis for a master power curve 555, as further representedin FIG. 11. The horizontal axis of the power curve represents thesaturation axis scan values and the vertical axis represents the sum ofsquares value (or output energy) for each output vector. In oneembodiment, as depicted in FIG. 11, each of the sum of squares isplotted with the magnitude of the sum of squares value plotted on thevertical “energy output” axis at the point on the horizontal axis of thecorresponding saturation scan value which generated that output vector.This results in a master power curve 558, an example of which isdepicted in FIG. 11. This provides a saturation transform in which thespectral content of the attenuated energy is examined by looking atevery possible saturation value and examining the output value for theassumed saturation value. As will be understood, where the inputs to themulti-variate process estimator 531 are mostly correlated, the sum ofsquares for the corresponding output vector of the multi-variate processestimator 531 will be very low. Conversely, where the correlationbetween the first and second inputs to the multi-variate processestimator 531 are not significantly correlated, the sum of squares ofthe output vector will be high. Accordingly, where the spectral contentof the reference signal and the first input to the multi-variate processestimator 531 are made up mostly of physiological (e.g., movement ofvenous blood due to respiration) and non-physiological (e.g., motioninduced) noise, the output energy will be low. Where the spectralcontent of the reference signal and the first input to the multi-variateprocess estimator 531 are not correlated, the output energy will be muchhigher.

A corresponding transform is completed by the Bin Power Curves module558, except a saturation transform power curve is generated for eachbin. The resulting power curves are provided as the outputs of thesaturation transform module 406.

In general, in accordance with the signal model embodiment of thepresent invention, there will be two peaks in the power curves, asdepicted in FIG. 11. One peak corresponds to the arterial oxygensaturation of the blood, and one peak corresponds to the venous oxygenconcentration of the blood. With reference to the signal model of thepresent invention, the peak corresponding to the highest saturationvalue (not necessarily the peak with the greatest magnitude) correspondsto the proportionality coefficient r_(a). In other words, theproportionality coefficient r_(a) corresponds to the ratio which will bemeasured for the arterial saturation. Similarly, peak that correspondsto the lowest saturation value (not necessarily the peak with the lowestmagnitude) will generally correspond to the venous oxygen saturation,which corresponds to the proportionality coefficient r_(v) in the signalmodel of the present invention. Therefore, the proportionalitycoefficient r_(v) will be a ratio corresponding to the venous oxygensaturation.

In order to obtain arterial oxygen saturation, the peak in the powercurves corresponding to the highest saturation value could be selected.However, to improve confidence in the value, further processing iscompleted. FIG. 12 illustrates the operation of the saturationcalculation module 408 based upon the output of the saturation transformmodule 406 and the output of the statistics module 404. As depicted inFIG. 12, the bin power curves and the bin statistics are provided to thesaturation calculation module 408. In the present embodiment, the masterpower curves are not provided to the saturation module 408 but can bedisplayed for a visual check on system operation. The bin statisticscontain the normalized RMS values for each of the wavelengths signals,the seed saturation value, and a value representing thecross-correlation between the various wavelengths's signals, asdescribed in greater detail above with respect to the statistics module404 of FIG. 9.

The saturation calculation module 408 first determines a plurality ofbin attributes as represented by the compute bin attributes module 560.The compute bin attributes module 560 collects a data bin from theinformation from the bin power curves and the information from the binstatistics. In the present embodiment, this operation involves placingthe saturation value of the peak from each power curve corresponding tothe highest saturation value in the data bin. In the present embodiment,the selection of the highest peak is performed by first computing thefirst derivative of the power curve in question by convolving the powercurve with a smoothing differentiator filter function. In the presentembodiment, the smoothing differentiator filter function (using a FIRfilter) has the following coefficients:

0.014964670230367

0.098294046682706

0.204468276324813

2.717182664241813

5.704485606695227

0.000000000000000

−5.704482606695227

−2.717182664241813

−0.204468276324813

−0.098294046682706

−0.014964670230367

This filter performs the differentiation and smoothing. Next, each pointin the original power curve in question is evaluated and determined tobe a possible peak if the following conditions are met: (1) the point isat least 2% of the maximum value in the power curve; and (2) the valueof the first derivative changes from greater than zero to less than orequal to zero. For each point that is found to be a possible peak, theneighboring points are examined and the largest of the three points isconsidered to be the true peak.

The peak width for these selected peaks is also calculated. The peakwidth of a power curve in question is computed by summing all the pointsin the power curve and subtracting the product of the minimum value inthe power curve and the number of points in the power curve. In thepresent embodiment, the peak width calculation is applied to each of thebin power curves. The maximum value is selected as the peak width.

In addition, the RMS value from the entire snapshot, the individualwavelengths's RMS values, the seed saturation value for each bin, andthe cross correlation between the n wavelengths's signals from thestatistics module 404 are also placed in the data bin. The attributesare then used to determine whether the data bin consists of acceptabledata, as represented in a bin qualifying logic module 562.

If the correlation is too low, the bin is discarded. If the saturationvalue of the selected peak for a given bin is lower than the seedsaturation for the same bin, the peak is replaced with the seedsaturation value. If any wavelength's RMS value is below a threshold,the bins are all discarded, and no saturation value is provided, becausethe measured signals are considered to be too small to obtain meaningfuldata. If no bins contain acceptable data, the exception handling module563 provides a message to the display 336 that the data is erroneous.

If some bins qualify, those bins that qualify as having acceptable dataare selected, and those that do not qualify are replaced with theaverage of the bins that are accepted. Each bin is given a time stamp inorder to maintain the time sequence. A voter operation 565 examines eachof the bins and selects the three highest saturation values. Thesevalues are forwarded to a clip and smooth operation 566.

The clip and smooth operation 566 performs averaging with a low passfilter. The low pass filter provides adjustable smoothing as selected bya select smoothing filter module 568. The select smoothing filter module568 performs its operation based upon a confidence determinationperformed by a high confidence test module 570. The high confidence testis an examination of the peak width for the bin power curves. The widthof the peaks provides some indication of motion by the patient, whereinwider peaks indicate motion. Therefore, if the peaks are wide, thesmoothing filter is slowed down. If peaks are narrow, the smoothingfilter speed is increased. Accordingly, the smoothing filter 566 isadjusted based on the confidence level. The output of the clip andsmooth module 566 provides the oxygen saturation values in accordancewith one embodiment of the present invention.

In one embodiment, the clip and smooth filter 566 takes each newsaturation value and compares it to the current saturation value. If themagnitude of the difference is less than 16 (percent oxygen saturation)then the value is pass. Otherwise, if the new saturation value is lessthan the filtered saturation value, the new saturation value is changedto 16 less than the filtered saturation value. If the new saturationvalue is greater than the filtered saturation value, then the newsaturation value is changed to 16 more than the filtered saturationvalue.

During high confidence (no motion), the smoothing filter is a simpleone-pole or exponential smoothing filter which in one embodiment iscomputed as follows:

y(n)=0.6*x(n)+0.4*y(n−1)

where x(n) is the clipped new saturation value, and y(n) is the filteredsaturation value.

During motion condition, a three-pole infinite impulse response (IIR)filter is used. Its characteristics are controlled by three timeconstants t_(a), t_(b), and t_(c) with values of 0.985, 0.900, and 0.94respectively. The coefficients for a direct form I, BR filter arecomputed from these time constants using the following relationships:

a ₀=0

a ₁ =t _(b)+(t _(c))(t _(a) +t _(b))

a ₂=(−t _(b))(t _(c))(t _(a) +t _(b)+(t _(c))(t _(a)))

a ₃=(t _(b))²(t _(c))²(t _(a))

b ₀=1−t _(b)−(t _(c))(t _(a)+(t _(c))(t _(b)))

b ₁=2(t _(b))(t _(c))(t _(a)−1)

b ₂=(t _(b))(t _(c))(t _(b)+(t _(c))(t _(a))−(t _(b))(t _(c))(t _(a))−t_(a))

It is well understood by those of skill in the art that the normalizedplethysmographic waveforms of the multi-wavelength physiological monitormay be utilized to determine the pulse rate of the patient. For example,in one embodiment, the normalized plethysmographic waveforms of themulti-wavelength physiological monitor, illustrated as lines λ₁ RMS, λ₂RMS, . . . λ_(n) RMS in FIG. 9, or the outputs 542 of FIG. 10 may beused to determine the patient's pulse rate. In one embodiment, a Fouriertransform is performed on at least one of the normalizedplethysmographic waveforms to convert the data of the waveform from thetime domain into the frequency domain using methods well known to thoseof skill in the art. In one embodiment, the first harmonic of thefrequency data is identified as the patient's pulse rate.

Other methods of determining pulse rate or heart rate from normalizedplethysmographic data is disclosed in U.S. Pat. No. 5,632,272,incorporated by reference in its entirety herein.

While a number of preferred embodiments of the invention and variationsthereof have been described in detail, other modifications and methodsof using and medical applications for the same will be apparent to thoseof skill in the art. Accordingly, it should be understood that variousapplications, modifications, and substitutions may be made ofequivalents without departing from the spirit of the invention or thescope of the claims.

1. A physiological monitor for determining blood oxygen saturation of apatient, the physiological monitor comprising: a pulse oximetry sensor,wherein the pulse oximetry sensor comprises: three or more lightemitting diodes, wherein each light emitting diode is operative to emitlight of a different wavelength; and a detector adapted to receive lightfrom the at least three light emitting diodes after being attenuated bytissue of a medical patient, wherein the detector provides an outputsignal based at least in part upon the received light; and a processorconfigured to compute a ratio based at least in part on three or moreplethysmographic signals obtained from the output signal, the processorfurther configured to determine the blood oxygen saturation of saidmedical patient based upon said ratio and one or more blood constituentsidentified from said output signal.
 2. The physiological monitor ofclaim 1, wherein the processor is further configured to determine theblood oxygen saturation of said medical patient by compensating theblood oxygen saturation with data regarding the one or more bloodconstituents.
 3. The physiological monitor of claim 1, wherein the ratiocomprises a quotient of a first value and a second value, wherein: thefirst value comprises a first sum of first electrical signalscorresponding to the three or more light emitting diodes, the firstelectrical signals multiplied by a first set of vector coefficients; andthe second value comprises a second sum of second electrical signalscorresponding to the three or more light emitting diodes, the secondelectrical signals multiplied by a second set of vector coefficients. 4.The physiological monitor of claim 3, wherein the first and second setsof vector coefficients are determined based upon calibration data. 5.The physiological monitor of claim 3, wherein the first and second setsof vector coefficients are determined based upon fitting of experimentaldata.
 6. The physiological monitor of claim 1, wherein the ratio issubstantially linear over a range of ratio values, wherein the range ofratio values comprises from about 0.45 to about 1.6.
 7. Thephysiological monitor of claim 1, wherein the ratio is substantiallylinear over a range of blood oxygen saturation values, wherein the rangeof blood oxygen saturation values comprises from about 75% to about 95%.8. The physiological monitor of claim 1, wherein the blood constituentcomprises methemoglobin.
 9. The physiological monitor of claim 1,wherein the blood constituent comprises carboxyhemoglobin.
 10. Thephysiological monitor of claim 1, wherein the blood constituentcomprises a low hemoglobin level.
 11. The physiological monitor of claim1, wherein the blood constituent comprises a high hemoglobin level. 12.The physiological monitor of claim 1, wherein the blood constituentcomprises bilirubin.
 13. The physiological monitor of claim 1, whereinthe blood constituent comprises methylene blue.
 14. The physiologicalmonitor of claim 1, wherein the blood constituent comprisesdeoxyhemoglobin.
 15. The physiological monitor of claim 1, wherein theblood constituent comprises lipids.
 16. The physiological monitor ofclaim 1, wherein the ratio is configured to have reduced tolerance atlower blood oxygen saturation levels compared to a two wavelength bloodoxygen saturation system.
 17. A physiological monitor sensor comprising:three or more emitters configured to transmit light through a tissuesite of a medical patient; and at least one detector configured to:measure the light transmitted through the tissue site of the medicalpatient by the three or more emitters; and generate at least one signalconfigured to be used by a processor to compute a ratio based at leastin part on the at least one signal, the processor further configured todetermine the blood oxygen saturation of said medical patient based uponsaid ratio and one or more blood constituents identified from saidsignal.
 18. The sensor of claim 17, wherein the sensor is capable ofremovable attachment to the tissue site of the medical patient.
 19. Thesensor of claim 17, wherein the three or more emitters comprise eightemitters.
 20. The sensor of claim 17, wherein the ratio is substantiallylinear over a range of blood oxygen saturation values, wherein the rangeof blood oxygen saturation values comprises from about 75% to about 95%.21. The sensor of claim 17, wherein a blood oxygen saturation of themedical patient calculated based at least in part on the ratio isconfigured to exhibit higher accuracy at lower blood oxygen saturationlevels compared to a two wavelength blood oxygen saturation system. 22.The sensor of claim 17, wherein the one or more blood constituentscomprises one or more of the following: methemoglobin,carboxyhemoglobin, a low hemoglobin level, a high hemoglobin level,bilirubin, methylene blue, deoxyhemoglobin, or lipids.
 23. A method ofdetermining the blood oxygen saturation of a patient, the methodcomprising: receiving at least three photoplethysmographic signalsindicative of at least three wavelengths of light that have beenattenuated by tissue; determining at least three output signals basedupon the at least three photoplethysmographic signals; calculating aratio based at least in part on the at least three output signals; anddetermining a blood oxygen saturation based at least in part on theratio and one or more blood constituents identified from said at leastthree photoplethysmographic signals.
 24. The method of claim 23, whereinthe ratio exhibits greater linearity over a first range of ratio valuescompared to a linearity over a second range of ratio values in atwo-wavelength blood oxygen saturation system.
 25. The method of claim23, further comprising selecting a data signal corresponding toattenuated light from one of the at least three photoplethysmographicsignals.
 26. The method of claim 25, further comprising providing aprocess estimator output using a multi-variate process estimator basedat least in part upon the ratio and the selected data signal, such thata blood oxygen saturation of a patient may be calculated in response tothe process estimator output.
 27. The method of claim 26, wherein themulti-variate process estimator comprises a pseudo-inverse.
 28. Themethod of claim 23, wherein the one or more blood constituents comprisesone or more of the following: methemoglobin, carboxyhemoglobin, a lowhemoglobin level, a high hemoglobin level, bilirubin, methylene blue,deoxyhemoglobin, or lipids.